The evolution of AI continues at high velocity.

With this rapid development Anthropic has submitted their recommendations to the U.S. Office of Science and Technology Policy (OSTP) “to address the economic and security implications of powerful AI while maximizing benefits” - Anthropic’s Recommendations to OSTP for the U.S. AI Action Plan \ Anthropic .

The DeepSeek panic is a revelation that competition and that other novel solutions built on the discoveries of others will propel AI forward.

There will be immeasurably more discoveries, but to actually benefit requires discipline and patience to apply them in real life...

Previous Executive Orders have been revoked by the new administration. As a result there will be impacts to the actions that are in progress and whether they proceed, are modified or get cancelled entirely.

The following was previously reported as part of the Biden administration’s actions concerning AI:

We’re often critical with the pace of progress within government agencies, but regarding the subject of Artificial Intelligence (AI) there’s a new urgency to advance the field, compliance and skills.

New regulations are now established to regulate AI safety including demonstrating test results for major AI models, the responsible use of AI and foreign entity utilization of U.S. cloud services for training AI models for national security, U.S. investments in critical national security technologies.

An AI Talent Surge began to attract top talent in the field of AI including the introduction of flexible hiring practices to fill positions across government departments.

And the National Science Foundation launched EducateAI to expand AI education from K-12 to prepare today’s students for tomorrow’s workforce.

These announcements recognize the importance of acting on the risks and prospects associated with AI while recognizing the necessity for advancement and its continued development. The origin of these initiatives can be referenced within the Executive Order published January 29, 2024.

Recognizing Human Innovation through Computation and AI for Chemistry ~ While the focus at Shokunindo is on the contribution we can collectively make to improve ourselves and the world around us, last week The Nobel Prize was awarded to several individuals who are doing just that. David Baker was awarded half of the prize “for computational protein design” with the other half shared by Demis Hassabis and John M. Jumper “for protein structure prediction” – according to The Nobel Prize announcement. These awards demonstrate the creative solutions we are capable of when comprehensively applying technology to solve problems. David Baker’s advancements to create de novo proteins, and Demis Hassabis and John M. Jumper’s impact on protein research capabilities, are interesting in that together they address both induced and deduced protein design. Congratulations to all of you on this remarkable achievement! You are shining examples for us all to model in our own endeavors! Read more at The Nobel Prize, The University of Washington, and Google DeepMind websites.

Recognizing Human Innovation, how Studies in Physics led to Foundational Discoveries for AI ~ At Shokunindo much of the emphasis is on putting AI to use in our own lives, but there are many who were inspired to pursue the challenges they saw in the real world, and were led to develop the underpinnings of the AI technology we take for granted today. John J. Hopfield “created a structure that can store and reconstruct information”, and Geoffrey Hinton “invented a method that can independently discover properties in data” – according to The Nobel Prize announcement. The discoveries by both of them were born from the necessity to solve problems exposed by limits in research, and the creativity needed to overcome barriers to understanding the physical world. Interestingly, the award and relationship of the two laureates are intertwined in that John J. Hopfield invented the Hopfield Network that utilizes nodes and a stepwise method to identify common patterns from atoms to images, and Geoffrey Hinton applied the Hopfield Network as a primer for a new method to identify patterns which are used for machine learning. Both are wonderful examples of what we can accomplish when we dare to imagine new ways to overcome the challenges we each face in our own pursuits! Read more at The Nobel Prize, Computer Science – The University of Toronto, and Princeton University websites.

With AI agents being actively pursued to perform or augment specific functions or occupations, the progress is being driven by familiar and burgeoning organizations with broad and narrow objectives. These new technologies are not just for the tech-savvy, rather their range of function provides approachable capabilities to implement and scale productivity and individual effectiveness. Google’s Project Astra, Whitespace’s Collective and newcomer MultiOn are but a few making an impression on the future of how work gets done.

As agents become more prevalent and effective, our roles must also evolve as we see agents assuming greater responsibilities by automating repetitive tasks, assisting diagnostics for decision support, and accelerating research and the discovery of solutions across industries.

Project Astra - Google DeepMind

AI Agents - Capabilities - Collective by Whitespace

MultiOn AI

NATO released a revised AI strategy on July 10, 2024, focusing on advancing responsible AI use, increasing interoperability, and addressing AI-enabled disinformation and gender-based violence. A key recognition is the critical dependency on reliable high-quality data required for AI to be its most effective.

This strategy aims to enhance security and ethical standards in the development and deployment of AI technologies within NATO member countries.

NATO - Official text: Summary of NATO's revised Artificial Intelligence (AI) strategy, 10-Jul.-2024

Boston Dynamics showcased new capabilities of its humanoid robot, Atlas, moving their current technology forward in dexterity and practical applications. These advancements present further commercial opportunities in manufacturing, logistics, and potential rescue operations.

Reference:

Atlas | Boston Dynamics

The minds at MIT continue to make significant improvements to maximize performance through continued optimization with machine-learning and the use of less data. These advancements enable more adaptive solutions without strict dependencies on referential data.

MIT has also advanced neural network control which will greatly improve complex systems including industrial technology and autonomous vehicles where safety and stability are critical.

Find more about these subjects at MIT Research Team (MIT News).

With the majority of our planet covered in water the team at Binghampton University is developing robotic solutions that are more durable, have long functional lives and capable of working in diverse conditions vs. current sensory and collection platforms.

Harnessing the potential of bacteria for power generation, these robots will be capable of sustaining operations for decades, using resources from the environment around them. monitoring is taking a step toward durability and scaling for environmental monitoring.

Find more at BingUNews.

An industry in and of itself, the focus on Advanced Driver Automation Systems (ADAS) and Autonomous Vehicles (AV) showcased the latest developments in San Jose, CA August 28th & 29th.

The diversity and convergence of technologies for ADAS/AV requires cooperation for research, AI, Big Data, analytics, manufacturing, testing, validation and regulatory compliance among others. The ADAS & Autonomous Vehicle Expo (August 2024) brings together the experts across these fields.